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Converts natural language questions into SQL queries and fetches data from your connected data warehouses. Results are automatically loaded as pandas DataFrames into the Python sandbox.
This is Ana’s primary tool for data retrieval from your connected databases and warehouses.

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How It Works

When you ask a question, Ana intelligently processes your request through these steps:
1

Analyze Your Question

Ana parses your natural language question to understand what data you’re looking for.
2

Examine Database Schema

Inspects your database structure to identify relevant tables and columns.
3

Generate SQL Query

Constructs an optimized SQL query in your warehouse’s specific dialect.
4

Execute Query

Runs the query against your data warehouse securely.
5

Load Results

Automatically loads results as a pandas DataFrame in the Python sandbox for further analysis.
SQL Query Tool in action

SQL Query Tool in action

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When to Use

Data Exploration

“Show me the top 10 customers by revenue”

Trend Analysis

“What are our monthly sales for the past year?”

Aggregations

“Calculate the average order value by region”

Filtering & Joining

“Find all orders from customers in California with a value over $1000”

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Available For

All connected database connectors:
  • Snowflake - Cloud data platform
  • BigQuery - Google Cloud data warehouse
  • Redshift - Amazon Web Services data warehouse
  • Databricks - Lakehouse platform
  • Azure Synapse Analytics - Microsoft cloud analytics
  • Postgres - Open source relational database
  • MySQL - Popular open source database
  • Amazon Aurora MySQL - AWS managed database
  • Supabase - Open source Firebase alternative
  • ClickHouse - OLAP database for analytics
  • Motherduck - Serverless analytics
  • Athena - AWS serverless query service

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Best Practices

Pro Tip: The more specific and detailed your question, the more accurate the generated SQL will be!

Be Specific

Include details like column names, table names, and exact metrics you want to calculate.Good: “Show me total revenue from the orders table grouped by month”Vague: “Show me sales”

Mention Table Names

If you know the table names, include them in your question to help Ana target the right data.Example: “Query the customers table for users who signed up this year”

Specify Time Ranges

Always include date ranges when querying time-series data for better performance.Example: “Sales from January 1, 2024 to March 31, 2024”

Review the Query

Check the generated SQL to ensure it matches your intent and doesn’t miss any important filters.You can view both the SQL query and the results in the chat interface.
Performance Note: For large tables, be specific with your filters and time ranges to avoid long-running queries that may time out.